Dash

Column

+24 %

+31 %

Column

+16 %

+28 %

Journals

Row

Unique Item Requests (TR_J1) 2020 im Vergleich zum Vorjahr in Prozent

Unique Item Requests (TR_J1) 2019 und 2020

Readme Definition Unique Item Requests (TR_J1)

Unique_Item_Requests: Es wird nur ein Zugriff gezählt, wenn beim Aufruf eines Artikels automatisch die HTML-Version geöffnet und anschließend das PDF des Artikels heruntergeladen wird.

Weitere Informationen zu den Metriken von COUNTER 5

Row

Elsevier Journals Unique Item Requests (TR_J1) 2019 und 2020

HighWire Journals Unique Item Requests (TR_J1) 2019 und 2020

Row

LWW Journals Unique Item Requests (TR_J1) 2019 und 2020

NEJM Journals Unique Item Requests (TR_J1) 2019 und 2020

Row

Springer Journals Unique Item Requests (TR_J1) 2019 und 2020

Wiley Journals Unique Item Requests (TR_J1) 2019 und 2020

E-Books

Column

Elsevier E-Books Unique Title Requests (TR_B1) 2019 und 2020

Column

Springer E-Books Unique Title Requests (TR_B1) 2019 und 2020

Primo

Column

Primo: Anzahl Suchen von 2018 bis 2020

Website

Column

Website: Besuche und Seitenansichten 2018 bis 2020

Website: Verwendung Betriebssysteme 2020

Column

Website: Besuche 2018 bis 2020

Website: Unique Users 2018 bis 2020

---
title: "Nutzungsstatistiken 2020"
# author: "Jan Taubitz"
# contact: "jan.taubitz@charite.de"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: scroll
    source_code: embed
editor_options: 
  chunk_output_type: console
---

 






```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE,
                      message = FALSE,
                      warning = FALSE,
                      options(scipen = 999))

library(flexdashboard)
library(readxl)
library(tidyverse)
library(lubridate)
library(gt)
library(janitor)
library(scales)
#library(DT)
library(plotly)
library(gameofthrones)
library(RColorBrewer)
```

Dash {data-icon="fa-globe"}
===================================== 

```{r}
# color pallettes for valueBoxes
pal <- got(20, option = "Jon_Snow", direction = 1)
pal <- str_sub(pal, end=-3)
```


Column
-----------------------------------------------------------------------


###

```{r}
valueBox(value = "",
         caption = "Elektr. Ressourcen",
         color = "#777B7C")
```


###

```{r}
perc_change <- "+24 %"
valueBox(perc_change,
         caption = "Downloads aus E-Journals 
(im Vergleich zum Vorjahr 2019)", icon = "fa-file", color = pal[18], href = "#journals") ``` ### ```{r} perc_change <- "+31 %" valueBox(perc_change, caption = "Downloads aus E-Books
(im Vergleich zum Vorjahr 2019)", icon = "fa-book", color = pal[20], href = "#e-books") ``` Column ----------------------------------------------------------------------- ### ```{r} valueBox(value = "", caption = "Primo und Website", color = "#777B7C") ``` ### ```{r} perc_change <- "+16 %" valueBox(perc_change, caption = "Suchen in Primo
(im Vergleich zum Vorjahr 2019)", icon = "fa-search-plus", color = pal[17], href = "#primo") ``` ### ```{r} perc_change <- "+28 %" valueBox(perc_change, caption = "Besuche der Website
(im Vergleich zum Vorjahr 2020)", icon = "fa-user-check", color = pal[19], href = "#website") ``` ```{r, eval=FALSE} ### #Die Zahl bezieht sich auf die prozentuale Veränderung zum Vorjahr 2019. ### # Elektronische Ressourcen ### # Bibliotheksportal und Website ### # Bibliothek als physischer Ort ``` ```{r, eval=FALSE} Elsevier_TM <- read.csv("T:/Statistik/ALMA_ART/Rohdaten_Counter_Master/Elsevier/Elsevier_Title_Master_Report.csv", header = T, skip = 12, sep = ",") ``` ```{r, eval=FALSE} unique(Elsevier_TM$Access_Method) unique(Elsevier_TM$Publisher) sapply(Elsevier_TM, function(x) length(unique(x))) sort(table(Elsevier_TM$Metric_Type), decreasing = TRUE) elsevier_tm_test <- Elsevier_TM %>% filter(Metric_Type == "Unique_Item_Requests" | Metric_Type == "Unique_Title_Requests" | Metric_Type == "No_License") %>% mutate(Total_2019 = rowSums(.[18:29])) %>% mutate(Total_2020 = rowSums(.[30:41])) %>% select(-c(3, 5, 15, 17:41)) elsevier_tm_test_2 <- Elsevier_TM %>% filter(Metric_Type == "Unique_Item_Requests" | Metric_Type == "Unique_Title_Requests" | Metric_Type == "No_License") %>% mutate(Total_2019 = rowSums(.[18:29])) %>% mutate(Total_2020 = rowSums(.[30:41])) %>% select(-c(3, 5, 15, 17:41)) %>% group_by(.[1:8], Data_Type, Section_Type, Access_Type, Metric_Type) %>% summarize_at(vars(Total_2019:Total_2020), sum) elsevier_tm_test_3 <- elsevier_tm_test_2 %>% filter(Section_Type != "Chapter") %>% group_by(Data_Type, Access_Type, Metric_Type) %>% summarize_at(vars(Total_2019:Total_2020), sum) elsevier_tm_test_3_long <- elsevier_tm_test_3 %>% pivot_longer( cols = starts_with("Total"), names_to = "Total", values_drop_na = TRUE) elsevier_tm_test_3_long <- elsevier_tm_test_3_long %>% filter(Data_Type == "Journal") elsevier_tm_test_3_long$Access_Type[elsevier_tm_test_3_long$Metric_Type == "No_License" ] <- "No_License" ``` ```{r, eval=FALSE} p <- ggplot(elsevier_tm_test_3_long, aes(x = value, y = Data_Type, color = Access_Type)) + geom_point(size = 4) + theme_minimal() ggplotly(p) ``` ```{r, eval=FALSE} p <- ggplot(elsevier_tm_test_3_long,aes(x = value, y = Data_Type, group = Access_Type, color = Access_Type)) + geom_path(size = 1, lineend = "butt", arrow = arrow(type = "open")) + geom_point() + theme_minimal() ggplotly(p) # , alpha = Total # https://github.com/ropensci/plotly/issues/469 # https://www.r-bloggers.com/2013/01/using-line-segments-to-compare-values-in-r/ ``` Journals {data-orientation=rows} ===================================== Row {data-height=600, .tabset} ----------------------------------------------------------------------- ### **Unique Item Requests (TR_J1) 2020 im Vergleich zum Vorjahr in Prozent** ```{r} Springer_2019_20 <- read.csv("T:/Statistik/ALMA_ART/Rohdaten_Springer/Springer_counter5_tr_j1_2019-01-2020-12.csv", header = T, skip = 13, sep = ",") ``` ```{r} LWW_2019_20 <- read.csv("T:/Statistik/ALMA_ART/Rohdaten_Journals/LWW/LWW_2019_2020_TR_J1.csv", header = T, skip = 13, sep = ",") ``` ```{r} LWW_2019_20_J3 <- read.csv("T:/Statistik/ALMA_ART/Rohdaten_Journals/LWW/LWW_2019_2020_TR_J3.csv", header = T, skip = 13, sep = ",") ``` ```{r} Highwire_2019_20 <- read.csv("T:/Statistik/ALMA_ART/Rohdaten_Journals/Highwire/Highwire_2019_2020_TR_J1.csv", header = T, skip = 13, sep = ",") ``` ```{r} Elsevier_2019_20 <- read.csv("T:/Statistik/ALMA_ART/Rohdaten_Journals/Elsevier/Elsevier_2019_2020_TR_J1.csv", header = T, skip = 13, sep = ",") ``` ```{r} Wiley_2019_20 <- read.table("T:/Statistik/ALMA_ART/Rohdaten_Journals/Wiley/Wiley_2019_2020_TR_J1.tsv", sep = '\t', header = TRUE, skip = 13) ``` ```{r} NEJM_2019_20 <- read.table("T:/Statistik/ALMA_ART/Rohdaten_Journals/NEJM/NEJM_2019_2020_TR_J1.tsv", sep = '\t', header = TRUE, skip = 13) ``` ```{r} Elsevier <- Elsevier_2019_20 %>% group_by(Metric_Type) %>% summarise_at(vars(Jan.2019:Dec.2020), sum) %>% filter(Metric_Type == "Unique_Item_Requests") %>% select(-1) %>% gather() %>% mutate(publisher = "Elsevier") %>% mutate(date = my(key)) %>% mutate(month = as.factor(month(date, label = T, abb = T))) %>% mutate(year = as.factor(year(date))) ``` ```{r} LWW <- LWW_2019_20 %>% group_by(Publisher, Metric_Type) %>% summarise_at(vars(Jan.2019:Dec.2020), sum) %>% filter(Metric_Type == "Unique_Item_Requests") %>% filter(Publisher == "Lippincott Williams & Wilkins (LWW)") %>% ungroup() %>% select(-1, -2) %>% gather() %>% mutate(publisher = "LWW") %>% mutate(date = my(key)) %>% mutate(month = as.factor(month(date, label = T, abb = T))) %>% mutate(year = as.factor(year(date))) ``` ```{r} Highwire <- Highwire_2019_20 %>% mutate(across(everything(), ~replace_na(.x, 0))) %>% group_by(Metric_Type) %>% summarise_at(vars(Jan.2019:Dec.2020), sum) %>% filter(Metric_Type == "Unique_Item_Requests") %>% select(-1) %>% gather() %>% mutate(publisher = "HighWire") %>% mutate(date = my(key)) %>% mutate(month = as.factor(month(date, label = T, abb = T))) %>% mutate(year = as.factor(year(date))) ``` ```{r} NEJM <- NEJM_2019_20 %>% group_by(Metric_Type) %>% summarise_at(vars(Jan.2019:Dec.2020), sum) %>% filter(Metric_Type == "Unique_Item_Requests") %>% select(-1) %>% gather() %>% mutate(publisher = "NEJM") %>% mutate(date = my(key)) %>% mutate(month = as.factor(month(date, label = T, abb = T))) %>% mutate(year = as.factor(year(date))) ``` ```{r} Springer <- Springer_2019_20 %>% group_by(Metric_Type) %>% summarise_at(vars(Jan.2019:Dec.2020), sum) %>% filter(Metric_Type == "Unique_Item_Requests") %>% select(-1) %>% gather() %>% mutate(publisher = "Springer") %>% mutate(date = my(key)) %>% mutate(month = as.factor(month(date, label = T, abb = T))) %>% mutate(year = as.factor(year(date))) ``` ```{r} Wiley <- Wiley_2019_20 %>% group_by(Metric_Type) %>% summarise_at(vars(Jan.2019:Dec.2020), sum) %>% filter(Metric_Type == "Unique_Item_Requests") %>% select(-1) %>% gather() %>% mutate(publisher = "Wiley") %>% mutate(date = my(key)) %>% mutate(month = as.factor(month(date, label = T, abb = T))) %>% mutate(year = as.factor(year(date))) ``` ```{r} Journals <- bind_rows(Elsevier, Highwire, LWW, NEJM, Springer, Wiley) ``` ```{r} Journals_Perc <- Journals %>% group_by(publisher, month) %>% arrange(month, year) %>% mutate(percent_change = (value / (lag(value)) - 1)) %>% mutate(percent_change = percent_change * 100) %>% filter(year == 2020) %>% select(-1,-2) publisher_uniq <- unique(Journals$publisher) ``` ```{r eval=FALSE} Journals_Perc %>% group_by(year) %>% summarise(mean = mean(percent_change)) ``` ```{r} pal <- got(length(publisher_uniq), option = "Jon_Snow", direction = 1) p1 <- ggplot(Journals_Perc, aes(x = month, y = percent_change, color = publisher)) + geom_point(size = 2) + geom_line(aes(x = month, y = percent_change, group = publisher), size = 1) + geom_hline(yintercept = 0, linetype = "dashed", alpha = 0.8) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_color_manual(values = pal) + # ylim(0, NA) + labs( title = "", y = "Veränderung in Prozent", x = "", color = "" ) ggplotly(p1) ``` ### **Unique Item Requests (TR_J1) 2019 und 2020** ```{r} pal <- got(length(publisher_uniq), option = "Jon_Snow", direction = 1) p1 <- ggplot(Journals, aes(x = month, y = value, color = publisher)) + geom_point(size = 2) + geom_line(aes(x = month, y = value, group = publisher), size = 1) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_color_manual(values = pal) + ylim(0, NA) + labs( title = "", y = "Unique Item Requests", x = "", color = "" ) + facet_wrap(~year) ggplotly(p1) ``` ### **Readme Definition Unique Item Requests (TR_J1)** Unique_Item_Requests: Es wird nur ein Zugriff gezählt, wenn beim Aufruf eines Artikels automatisch die HTML-Version geöffnet und anschließend das PDF des Artikels heruntergeladen wird. [Weitere Informationen zu den Metriken von COUNTER 5](http://www.bib-bvb.de/documents/11183/10148261/COUNTER-5_Zusammenstellung_KER_2020-01.pdf/bfaa960a-30ed-4429-9a99-1328a0598c44) Row ----------------------------------------------------------------------- ### **Elsevier Journals Unique Item Requests (TR_J1) 2019 und 2020** ```{r} pal <- got(2, option = "Jon_Snow", direction = 1) p1 <- ggplot(Elsevier, aes(x = month, y = value, color = year)) + geom_point(size = 2) + geom_line(aes(x = month, y = value, group = year), size = 1) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_color_manual(values = pal) + # ylim(0, NA) + labs( title = "", y = "Unique Item Requests", x = "", color = "" ) ggplotly(p1) ``` ### **HighWire Journals Unique Item Requests (TR_J1) 2019 und 2020** ```{r} pal <- got(2, option = "Jon_Snow", direction = 1) p1 <- ggplot(Highwire, aes(x = month, y = value, color = year)) + geom_point(size = 2) + geom_line(aes(x = month, y = value, group = year), size = 1) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_color_manual(values = pal) + # ylim(0, NA) + labs( title = "", y = "Unique Item Requests", x = "", color = "" ) ggplotly(p1) ``` Row ----------------------------------------------------------------------- ### **LWW Journals Unique Item Requests (TR_J1) 2019 und 2020** ```{r} pal <- got(2, option = "Jon_Snow", direction = 1) p1 <- ggplot(LWW, aes(x = month, y = value, color = year)) + geom_point(size = 2) + geom_line(aes(x = month, y = value, group = year), size = 1) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_color_manual(values = pal) + # ylim(0, NA) + labs( title = "", y = "Unique Item Requests", x = "", color = "" ) ggplotly(p1) ``` ### **NEJM Journals Unique Item Requests (TR_J1) 2019 und 2020** ```{r} pal <- got(2, option = "Jon_Snow", direction = 1) p1 <- ggplot(NEJM, aes(x = month, y = value, color = year)) + geom_point(size = 2) + geom_line(aes(x = month, y = value, group = year), size = 1) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_color_manual(values = pal) + # ylim(0, NA) + labs( title = "", y = "Unique Item Requests", x = "", color = "" ) ggplotly(p1) ``` Row ----------------------------------------------------------------------- ### **Springer Journals Unique Item Requests (TR_J1) 2019 und 2020** ```{r} pal <- got(2, option = "Jon_Snow", direction = 1) p1 <- ggplot(Springer, aes(x = month, y = value, color = year)) + geom_point(size = 2) + geom_line(aes(x = month, y = value, group = year), size = 1) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_color_manual(values = pal) + # ylim(0, NA) + labs( title = "", y = "Unique Item Requests", x = "", color = "" ) ggplotly(p1) ``` ### **Wiley Journals Unique Item Requests (TR_J1) 2019 und 2020** ```{r} pal <- got(2, option = "Jon_Snow", direction = 1) p1 <- ggplot(Wiley, aes(x = month, y = value, color = year)) + geom_point(size = 2) + geom_line(aes(x = month, y = value, group = year), size = 1) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_color_manual(values = pal) + # ylim(0, NA) + labs( title = "", y = "Unique Item Requests", x = "", color = "" ) ggplotly(p1) ``` ```{r eval=FALSE} Springer %>% group_by(year) %>% summarise(value = sum(value)) %>% mutate(percent_change = (value / (lag(value)) - 1)) ``` ```{r eval=FALSE} LWW %>% group_by(year) %>% summarise(value = sum(value)) %>% mutate(percent_change = (value / (lag(value)) - 1)) ``` ```{r eval=FALSE} Journals %>% group_by(year) %>% summarise(value = sum(value)) %>% mutate(percent_change = (value / (lag(value)) - 1)) ``` E-Books {data-orientation=columns} ===================================== Column {data-width=400, data-height=500} ----------------------------------------------------------------------- ```{r} Elsevier_2019_20_Books <- read.csv("T:/Statistik/ALMA_ART/Rohdaten_E_Books/Elsevier/Elsevier_2019_2020_TR_B1.csv", header = T, skip = 13, sep = ",") ``` ```{r} Springer_2019_20_Books <- read.csv("T:/Statistik/ALMA_ART/Rohdaten_E_Books/Springer/Springer_2019_2020_TR_B1.csv", header = T, skip = 13, sep = ",") ``` ```{r} Elsevier <- Elsevier_2019_20_Books %>% group_by(Metric_Type) %>% summarise_at(vars(Jan.2019:Dec.2020), sum) %>% filter(Metric_Type == "Unique_Title_Requests") %>% select(-1) %>% gather() %>% mutate(publisher = "Elsevier") %>% mutate(date = my(key)) %>% mutate(month = as.factor(month(date, label = T, abb = T))) %>% mutate(year = as.factor(year(date))) ``` ```{r} Springer <- Springer_2019_20_Books %>% group_by(Metric_Type) %>% summarise_at(vars(Jan.2019:Dec.2020), sum) %>% filter(Metric_Type == "Unique_Title_Requests") %>% select(-1) %>% gather() %>% mutate(date = my(key)) %>% mutate(month = as.factor(month(date, label = T, abb = T))) %>% mutate(year = as.factor(year(date))) ``` ### **Elsevier E-Books Unique Title Requests (TR_B1) 2019 und 2020** ```{r} pal <- got(2, option = "Jon_Snow", direction = 1) p1 <- ggplot(Elsevier, aes(x = month, y = value, color = year)) + geom_point(size = 2) + geom_line(aes(x = month, y = value, group = year), size = 1) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_color_manual(values = pal) + # ylim(0, NA) + labs( title = "", y = "Unique Title Requests", x = "", color = "" ) ggplotly(p1) ``` Column {data-width=400, data-height=500} ----------------------------------------------------------------------- ### **Springer E-Books Unique Title Requests (TR_B1) 2019 und 2020** ```{r} pal <- got(2, option = "Jon_Snow", direction = 1) p1 <- ggplot(Springer, aes(x = month, y = value, color = year)) + geom_point(size = 2) + geom_line(aes(x = month, y = value, group = year), size = 1) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_color_manual(values = pal) + # ylim(0, NA) + labs( title = "", y = "Unique Title Requests", x = "", color = "" ) ggplotly(p1) ``` ```{r eval=FALSE} Springer %>% group_by(year) %>% summarise(value = sum(value)) %>% mutate(percent_change = (value / (lag(value)) - 1)) ``` Primo {data-orientation=columns} ===================================== Column {data-width=400, data-height=500} ----------------------------------------------------------------------- ### Primo: Anzahl Suchen von 2018 bis 2020 ```{r} Primo_Stat_Auswertungen <- read_excel( "T:/Statistik/ALMA_ART/Primo_Stat_Auswertungen.xlsx", sheet = "R_1", col_types = c("date", "text", "numeric") ) ``` ```{r} Primo <- Primo_Stat_Auswertungen %>% mutate(Year = year(Date)) %>% group_by(Year, Action) %>% summarise(Value = sum(Value)) ``` ```{r} Primo_3 <- Primo %>% summarise(Search_Total = sum(Value)) %>% mutate(Percent_Change = (Search_Total / (lag(Search_Total)) - 1)) ``` ```{r} pal <- got(2, option = "Jon_Snow", direction = 1) p1 <- ggplot(Primo, aes(x = Year, y = Value)) + geom_col(aes(fill = Action), position = "dodge") + theme_classic() + labs( title = "", subtitle = "", fill = "Suchen", y = "", x = "" ) + scale_fill_manual(values = pal) + theme(legend.position = "bottom") ggplotly(p1) %>% layout(legend = list(orientation = "h", y = -0.1)) ``` ```{r eval=FALSE} #Column {data-width=200, data-height=200} #----------------------------------------------------------------------- ### Primo B Primo_2 <- Primo %>% spread(key = Year, value = Value) %>% adorn_totals("row") gt(Primo_3) %>% tab_header(title = md("Suchen in Primo")) ``` Website {data-orientation=columns} ===================================== Column ----------------------------------------------------------------------- ### Website: Besuche und Seitenansichten 2018 bis 2020 ```{r} Website_Besuche <- read_excel( "T:/Statistik/ALMA_ART/Website.xlsx", sheet = "Besuche", col_types = c("numeric", "text", "numeric") ) ``` ```{r} Website_Besuche_2 <- Website_Besuche %>% group_by(Action) %>% # filter(Action == "Besuche") %>% mutate(Percent_Change = (Value / (lag(Value)) - 1)) ``` ```{r} pal <- got(2, option = "Jon_Snow", direction = 1) p1 <- ggplot(Website_Besuche, aes(x = Date, y = Value)) + geom_col(aes(fill = Action), position = "dodge") + theme_classic() + scale_fill_manual(values = pal) + labs(title = "", fill = "", y = "", x = "") ggplotly(p1) ``` ### Website: Verwendung Betriebssysteme 2020 ```{r} Website_OS <- read_excel( "T:/Statistik/ALMA_ART/Website.xlsx", sheet = "Desktop", col_types = c("text", "text", "numeric") ) ``` ```{r} pal <- got(3, option = "Jon_Snow", direction = -1) p1 <- Website_OS %>% filter(date == "2020") %>% plot_ly(marker = list(colors = pal)) %>% add_pie(labels = Website_OS$action, values = Website_OS$value, hole = 0.6) %>% layout(title = "") p1 ``` Column ----------------------------------------------------------------------- ### Website: Besuche 2018 bis 2020 ```{r} Website_Users <- read_excel( "T:/Statistik/ALMA_ART/Website.xlsx", sheet = "Users", col_types = c("date", "numeric", "numeric") ) pal <- got(3, option = "Jon_Snow", direction = 1) ``` ```{r} Website_Users_2 <- Website_Users %>% mutate(month = as.factor(month(date, label = T, abb = T))) %>% mutate(year = as.factor(year(date))) p2 <- ggplot(Website_Users_2, aes(x = month, y = visits, color = year)) + geom_point(size = 2) + geom_line(aes(x = month, y = visits, group = year), size = 1) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_color_manual(values = pal) + labs( title = "", y = "Besuche", x = "", color = "" ) ggplotly(p2) ``` ### Website: Unique Users 2018 bis 2020 ```{r} Website_Users_2 <- Website_Users %>% mutate(month = as.factor(month(date, label = T, abb = T))) %>% mutate(year = as.factor(year(date))) p1 <- ggplot(Website_Users_2, aes(x = month, y = unique_users, color = year)) + geom_point(size = 2) + geom_line(aes(x = month, y = unique_users, group = year), size = 1) + theme(axis.text.x = element_text(angle = 90)) + # scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 month")+ theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + scale_color_manual(values = pal) + labs( title = "", y = "Unique Users", x = "", color = "" ) ggplotly(p1) ```